Author ORCID Identifier

https://orcid.org/0000-0003-2080-4172

Year of Publication

2018

Degree Name

Doctor of Philosophy (PhD)

Document Type

Doctoral Dissertation

College

Pharmacy

Department

Pharmaceutical Sciences

First Advisor

Dr. Esther P. Black

Second Advisor

Dr. Jeffery Talbert

Abstract

Precision medicine has allowed for the development of monoclonal antibodies that unmask the anti-tumor immune response. These agents have provided some patients durable clinical benefit. However, PD-1 and PD-L1 inhibitor therapies are effective in a small group (10-20%) of non-small cell lung cancer (NSCLC) patients when used as single-agent therapy. The approved companion diagnostic is expression of the immune cell surface molecule, programmed death ligand 1 (PD-L1), on tumors measured by immunohistochemistry (IHC). Studies in tumor biology and immune surveillance dictate that PD-1 inhibitor efficacy should depend on the level of PD-L1 expression; however, the literature has not followed with convincing evidence. The limitations of this test include timing of tissue acquisition, tumor heterogeneity, and timing of therapy relative to the expression of PD-L1. In addition, the requirement of analyzing tumor tissue biopsy samples from a patient is cumbersome. Thus, a peripheral blood biomarker that predicts efficacy of PD-1/PD-L1 inhibition would be optimal for precise and cost-effective treatment. A history of chronic inflammatory diseases may be advantageous for a cancer patient who is treated with PD-1/PD-L1 inhibitors and may allow them to then mobilize a swift immune response to tumor cells. Specific biological components of this persistent inflammation may predict PD-1 inhibitor response. We have taken a novel approach to leverage national healthcare claims data that couples patient history with response to therapy. We have identified potential peripheral blood biomarkers of response to PD-1/PD-L1 inhibitors using a combination of healthcare outcomes and molecular markers that correlate with therapeutic efficacy.

Digital Object Identifier (DOI)

https://doi.org/10.13023/ETD.2018.045

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